An Ultra-Low Sidelobe Pulse Compression Method of LFM Signal Based on CNN

Pulse compression technology is proposed to solve the contradiction between radar detection power and range resolution.The traditional matched filtering methods with weighting windows, such as Hamming window, only suppress the sidelobe level in a certain extent, however widen the mainlobe. In recent years, the signal processing methods based on convolutional neural network (CNN) are widely used in various fields. Based on CNN, a new pulse compression method of LFM signal is proposed, which can not only obtain almost ideal mainlobe width, but also obtain ultra-low sidelobe level. The proposed method is consisted of two stages and three subnetworks. In the stage-1, two 1-D CNNs are used to extract features from the real part and imaginary part of the LFM signal sequence and complete preliminary pulse compression. Then, based on outputs of the stage-1 subnetworks, the subnetwork with one hidden layer in stage-2 is used to achieve final pulse compression. Compared with traditional matched filtering methods,the superiority of the proposed method is validated by numerical simulations, that the method can obtain extremely narrow mainlobe and suppress the peak sidelobe level below -85 dB.